Transcriptomics: Lecture 1

Biotech 7005/Bioinf 3000
Frontiers of Biotechnology: Bioinformatics and Systems Modelling
The University of Adelaide

Author
Affiliation


Dr Stevie Pederson (They/Them)

Black Ochre Data Labs, The Kids Research Institute Australia

Published

September 1, 2025

Welcome To Country

I’d like to acknowledge the Kaurna people as the traditional owners and custodians of the land we know today as the Adelaide Plains, where I live & work.

I also acknowledge the deep feelings of attachment and relationship of the Kaurna people to their place.

I pay my respects to the cultural authority of Aboriginal and Torres Strait Islander peoples from other areas of Australia, and pay our respects to Elders past, present and emerging, and acknowledge any Aboriginal Australians who may be with us today

Introduction

  • Postdoctoral Bioinformatician, Black Ochre Data Labs, Adelaide
  • Working in collaboration with members of the SA Aboriginal community
  • Multi-omics project to identify and address the underlying causes of high T2D rates and complications
    • Using genomics, epigenomics, transcriptomics and other layers
    • My focus is on the transcriptomics layer

Why Transcriptomics?

  • DNA can be described as being like a giant book of instructions
  • Some regions are defined as genes
    • Originally considered to be the basic unit of inheritance
    • Now commonly used to describe a region of DNA transcribed into RNA

Why Transcriptomics?

  • DNA \(\rightarrow\) mRNA \(\rightarrow\) Proteins
    • Commonly referred to as the Central Dogma of Biology
  • Proteins are the workhorses of the cell & body
    • They do most of the work, and are responsible for most of the structure
    • Things like keratin (hair), haemoglobin (oxygen transport) etc
  • ncRNAs are also highly functional
    • Ribosomal RNA (rRNA) essential for translation from mRNA to Protein
    • microRNAs play a role in gene-regulation via mRNA stability
    • lncRNAs: Xist coats the entire X chromosome during X inactivation

Why Transcriptomics?

Definition

The transcriptome can be defined as the complete set of (RNA) transcripts in a cell, or a population of cells, for a specific developmental stage or physiological condition (Wang, Gerstein, and Snyder 2009)

  • Transcriptomics is simply the study of the transcriptome
  • Can be the entire RNA content of a cell (or cells) or a subset of molecules (e.g. mRNA)

Why Transcriptomics?

Taken from Fang et al. (2015)
  • Most RNA is single-stranded but can have extremely complex structure
    • Shown is a 2kb region from the lncRNA Xist (17kb in total)
  • Also interacts with the antisense lncRNA Tsix

Why Transcriptomics?

  • Is a snapshot of the dynamic biological processes associated with a biological question
  • Use to make inference about these processes
    • Identify therapeutic targets for Cardiovascular Disease
    • Biomarkers for CAR-T cells
    • Key drivers of correlated gene networks
    • Early drivers of neurodegeneration in Alzheimers
  • Assumed to be low-level
    • DNA \(\rightarrow\) RNA \(\rightarrow\) Protein \(\rightarrow\) Metabolites, Signalling molecules, etc …

Why Transcriptomics?

  • Is the first molecular level where quantity becomes relevant
    • Highly-expressed, or low-expressed genes are important
    • Changes in response to stimulus impact gene expression levels
  • Much of the early transcriptomic analyses were quantitative
    • Sequence variation often captured at DNA-level
  • Now extending to transcript structure and modifications
    • Identification of fusion transcripts, RNA-methylation etc

Why Transcriptomics?

  • Early techniques were often using large numbers of cells
    • Often multiple cell types withing a bilogical sample
  • Modern techniques are incredibly detailed
    • Single-Cell RNA characterises exact cell types and cell trajectories
    • Spatial transcriptomics used to identify co-located cells in tissue

References

Fang, Rui, Walter N Moss, Michael Rutenberg-Schoenberg, and Matthew D Simon. 2015. “Probing Xist RNA Structure in Cells Using Targeted Structure-Seq.” PLoS Genet. 11 (12): e1005668.
Wang, Zhong, Mark Gerstein, and Michael Snyder. 2009. “RNA-Seq: A Revolutionary Tool for Transcriptomics.” Nat. Rev. Genet. 10 (1): 57–63.